B-Spline Neural Networks Based PID Controller for Hammerstein Systems

  • Xia Hong
  • Serdar Iplikci
  • Sheng Chen
  • Kevin Warwick
Conference paper

DOI: 10.1007/978-3-642-31837-5_6

Volume 304 of the book series Communications in Computer and Information Science (CCIS)
Cite this paper as:
Hong X., Iplikci S., Chen S., Warwick K. (2012) B-Spline Neural Networks Based PID Controller for Hammerstein Systems. In: Huang DS., Gupta P., Zhang X., Premaratne P. (eds) Emerging Intelligent Computing Technology and Applications. ICIC 2012. Communications in Computer and Information Science, vol 304. Springer, Berlin, Heidelberg

Abstract

A new PID tuning and controller approach is introduced for Hammerstein systems based on input/output data. A B-spline neural network is used to model the nonlinear static function in the Hammerstein system. The control signal is composed of a PID controller together with a correction term. In order to update the control signal, the multistep ahead predictions of the Hammerstein system based on the B-spline neural networks and the associated Jacobians matrix are calculated using the De Boor algorithms including both the functional and derivative recursions. A numerical example is utilized to demonstrate the efficacy of the proposed approaches.

Keywords

Hammerstein model PID controller system identification 

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Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Xia Hong
    • 1
  • Serdar Iplikci
    • 2
  • Sheng Chen
    • 3
  • Kevin Warwick
    • 4
  1. 1.School of Systems EngineeringUniversity of ReadingUK
  2. 2.Department of Electrical and Electronics EngineeringPamukkale UniversityDenizliTurkey
  3. 3.School of Electronics and Computer ScienceUniversity of SouthamptonUK
  4. 4.Faculty of EngineeringKing Abdulaziz UniversityJeddahSaudi Arabia